This dashboard shows results from the Berkeley Interpersonal Contact Study (BICS) in Spring 2020.
Caveats / underway:
except where noted, these results show the national and city samples pooled together
the values presented here have not yet been adjusted to improve sample representativeness
If you have questions or want more information, please contact us at bics@demog.berkeley.edu.
Initial support provided by a Berkeley Population Center pilot grant (NICHD P2CHD073964). If you are interested in funding us, please reach out! This project has been approved by the UC Berkeley IRB (Protocol 2020-03-13128).
NB: please see the ‘data’ tab if you want the numbers behind these mixing estimates
# A tibble: 16 x 6
# Groups: ego_age [4]
ego_age alter_age bics fb ratio frac_decrease
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 [25,35) [25,35) 1.09 7.16 0.152 0.848
2 [25,35) [35,45) 0.5 2.36 0.212 0.788
3 [25,35) [45,65) 0.451 1.25 0.361 0.639
4 [25,35) [65,100] 0.112 0.173 0.646 0.354
5 [35,45) [25,35) 0.513 3.29 0.156 0.844
6 [35,45) [35,45) 1.29 5.87 0.220 0.780
7 [35,45) [45,65) 0.367 1.70 0.215 0.785
8 [35,45) [65,100] 0.202 0.528 0.383 0.617
9 [45,65) [25,35) 0.317 2.23 0.142 0.858
10 [45,65) [35,45) 0.411 3.10 0.132 0.868
11 [45,65) [45,65) 0.943 3.77 0.250 0.750
12 [45,65) [65,100] 0.319 0.755 0.422 0.578
13 [65,100] [25,35) 0.222 0.737 0.301 0.699
14 [65,100] [35,45) 0.322 2.14 0.150 0.850
15 [65,100] [45,65) 0.451 2.00 0.225 0.775
16 [65,100] [65,100] 0.791 2.17 0.364 0.636
To help summarize patterns in the contact survey data, we fit a negative binomial model, accounting for the right-censoring of reported contacts at 10. These models show relationships among people who have completed the survey; have not been adjusted in any way for sampling. We fit these models using the brms package in R.
We modeled the expected log number of reported contacts as a function of age group, city, gender, and household size. The plots below show posterior means and 95% credible intervals for the estimated coefficients. Estimated coefficients greater than 0 imply that the predictor is associated with higher reported numbers of contacts, while estimated coefficients less than 0 imply that the predictor is associated with lower reported numbers of contacts.
There are two models: one for total number of contacts, and one for the number of non-household contacts.
We fit negative binomial models to summarize the patterns in the data, and to account for right-censoring of reported contacts at 10.
That is, we model the observed reported number of conversational contacts, \(y_i\), as
\[ \begin{aligned} y_i &\sim \text{Poisson}(\lambda_i)\\ \lambda_i &\sim \text{Gamma}() \end{aligned} \]
We fit models of the form
$$ \begin{aligned}
i &= + {male[i]} + {age[i]} + {city[i]} + _{hhsize} _i, \end{aligned} $$
where \(\alpha\) is an intercept; \(i\) indexes survey respondents; \(\beta_{male[i]}\) is the coefficient on a dummy variable for whether or not respondent is male; \(\beta_{city[i]}\) is a dummy variable for the sample \(i\) comes from (national, or one of the city samples; reference group is the national sample); \(\text{hh}_i\) is \(i\)’s reported household size; and \(\beta_{hhsize}\) is the estimated coefficient on household size.
Respondents to the survey were told to consider someone a contact using this text:
We would like to ask you some questions about people you had in-person conversational contact with yesterday.
By in-person conversational contact, we mean a two-way conversation with three or more words in the physical presence of another person.
You might have conversational contact with family members, friends, co-workers, store clerks, bus drivers, and so forth.
(Please do not count people you contacted exclusively by telephone, text, or online. Only consider people you interacted with face-to-face.)
We plan to produce a version of the data with no identifying information publicly available as soon as we can. If you are a disease modeler who urgently needs to see the microdata, please reach out to us by email.
The estimated mixing matrices are reproduced as tables below. Note that these are the crude estimates, and have not had a symmetry constraint enforced.
| ego_age | alter_age | weighted_n | raw_n | num_interviews | avg_per_ego |
|---|---|---|---|---|---|
| [18,25) | [0,10) | 35.66667 | 25 | 185 | 0.1927928 |
| [18,25) | [10,18) | 68.75000 | 51 | 185 | 0.3716216 |
| [18,25) | [18,25) | 185.16667 | 154 | 185 | 1.0009009 |
| [18,25) | [25,35) | 81.00000 | 65 | 185 | 0.4378378 |
| [18,25) | [35,45) | 62.41667 | 49 | 185 | 0.3373874 |
| [18,25) | [45,65) | 110.75000 | 93 | 185 | 0.5986486 |
| [18,25) | [65,100] | 10.25000 | 9 | 185 | 0.0554054 |
| [25,35) | [0,10) | 74.33333 | 60 | 279 | 0.2664277 |
| [25,35) | [10,18) | 39.66667 | 29 | 279 | 0.1421744 |
| [25,35) | [18,25) | 72.33333 | 59 | 279 | 0.2592593 |
| [25,35) | [25,35) | 304.25000 | 243 | 279 | 1.0905018 |
| [25,35) | [35,45) | 139.50000 | 104 | 279 | 0.5000000 |
| [25,35) | [45,65) | 125.75000 | 104 | 279 | 0.4507168 |
| [25,35) | [65,100] | 31.16667 | 26 | 279 | 0.1117085 |
| [35,45) | [0,10) | 69.08333 | 57 | 298 | 0.2318233 |
| [35,45) | [10,18) | 98.25000 | 75 | 298 | 0.3296980 |
| [35,45) | [18,25) | 47.00000 | 37 | 298 | 0.1577181 |
| [35,45) | [25,35) | 152.75000 | 117 | 298 | 0.5125839 |
| [35,45) | [35,45) | 384.33333 | 299 | 298 | 1.2897092 |
| [35,45) | [45,65) | 109.25000 | 92 | 298 | 0.3666107 |
| [35,45) | [65,100] | 60.33333 | 54 | 298 | 0.2024609 |
| [45,65) | [0,10) | 51.41667 | 32 | 450 | 0.1142593 |
| [45,65) | [10,18) | 115.00000 | 90 | 450 | 0.2555556 |
| [45,65) | [18,25) | 96.25000 | 79 | 450 | 0.2138889 |
| [45,65) | [25,35) | 142.58333 | 122 | 450 | 0.3168519 |
| [45,65) | [35,45) | 185.08333 | 154 | 450 | 0.4112963 |
| [45,65) | [45,65) | 424.25000 | 357 | 450 | 0.9427778 |
| [45,65) | [65,100] | 143.41667 | 130 | 450 | 0.3187037 |
| [65,100] | [0,10) | 13.50000 | 12 | 213 | 0.0633803 |
| [65,100] | [10,18) | 10.91667 | 10 | 213 | 0.0512520 |
| [65,100] | [18,25) | 23.16667 | 21 | 213 | 0.1087637 |
| [65,100] | [25,35) | 47.25000 | 44 | 213 | 0.2218310 |
| [65,100] | [35,45) | 68.66667 | 59 | 213 | 0.3223787 |
| [65,100] | [45,65) | 96.00000 | 83 | 213 | 0.4507042 |
| [65,100] | [65,100] | 168.50000 | 148 | 213 | 0.7910798 |
| ego_age | alter_age | weighted_n | raw_n | num_interviews | avg_per_ego |
|---|---|---|---|---|---|
| [18,25) | [0,10) | 10.500000 | 8 | 185 | 0.0567568 |
| [18,25) | [10,18) | 12.500000 | 11 | 185 | 0.0675676 |
| [18,25) | [18,25) | 73.416667 | 61 | 185 | 0.3968468 |
| [18,25) | [25,35) | 37.833333 | 29 | 185 | 0.2045045 |
| [18,25) | [35,45) | 15.166667 | 13 | 185 | 0.0819820 |
| [18,25) | [45,65) | 9.916667 | 9 | 185 | 0.0536036 |
| [18,25) | [65,100] | 5.250000 | 4 | 185 | 0.0283784 |
| [25,35) | [0,10) | 4.000000 | 4 | 279 | 0.0143369 |
| [25,35) | [10,18) | 5.250000 | 4 | 279 | 0.0188172 |
| [25,35) | [18,25) | 30.250000 | 25 | 279 | 0.1084229 |
| [25,35) | [25,35) | 123.250000 | 96 | 279 | 0.4417563 |
| [25,35) | [35,45) | 52.833333 | 42 | 279 | 0.1893668 |
| [25,35) | [45,65) | 52.250000 | 40 | 279 | 0.1872760 |
| [25,35) | [65,100] | 16.833333 | 12 | 279 | 0.0603345 |
| [35,45) | [0,10) | 3.750000 | 3 | 298 | 0.0125839 |
| [35,45) | [10,18) | 5.000000 | 4 | 298 | 0.0167785 |
| [35,45) | [18,25) | 27.083333 | 21 | 298 | 0.0908837 |
| [35,45) | [25,35) | 73.750000 | 55 | 298 | 0.2474832 |
| [35,45) | [35,45) | 100.083333 | 83 | 298 | 0.3358501 |
| [35,45) | [45,65) | 59.166667 | 50 | 298 | 0.1985459 |
| [35,45) | [65,100] | 23.083333 | 20 | 298 | 0.0774609 |
| [45,65) | [0,10) | 7.250000 | 5 | 450 | 0.0161111 |
| [45,65) | [10,18) | 5.750000 | 5 | 450 | 0.0127778 |
| [45,65) | [18,25) | 27.000000 | 22 | 450 | 0.0600000 |
| [45,65) | [25,35) | 83.416667 | 73 | 450 | 0.1853704 |
| [45,65) | [35,45) | 105.416667 | 90 | 450 | 0.2342593 |
| [45,65) | [45,65) | 150.166667 | 128 | 450 | 0.3337037 |
| [45,65) | [65,100] | 66.000000 | 59 | 450 | 0.1466667 |
| [65,100] | [0,10) | 8.500000 | 8 | 213 | 0.0399061 |
| [65,100] | [10,18) | 7.916667 | 7 | 213 | 0.0371674 |
| [65,100] | [18,25) | 10.000000 | 9 | 213 | 0.0469484 |
| [65,100] | [25,35) | 34.000000 | 32 | 213 | 0.1596244 |
| [65,100] | [35,45) | 43.583333 | 36 | 213 | 0.2046166 |
| [65,100] | [45,65) | 54.500000 | 46 | 213 | 0.2558685 |
| [65,100] | [65,100] | 54.750000 | 49 | 213 | 0.2570423 |